基于箱线图异常检测的指数加权平滑预测模型

Exponential Weighted Smoothing Prediction Model Based on Abnormal Detection of Box-plot

数据预测模型是近年来无线传感网数据传输领域的研究热点。随着监测环境日益复杂和多样化,传感节点采集的数据集常伴有异常点,当前大多数预测模型并未过滤异常点。为了有效过滤异常点,提高数据传输的精简程度和预测准确度,本文提出一种基于箱线图异常检测的指数加权平滑预测模型,同时引入短期环比机制判定突发事件。实验表明,该模型在不同数据集、平滑系数变动和不同动态阈值下都能有效过滤异常点并判定出突发事件,数据传输的精简程度提高了5.8%,预测准确度提高了8.4%,与现有的预测模型相比具有更好的鲁棒性和异常点处理能力。

Data prediction model is a research hotspot in the field of data transmission in wireless sensor networks in recent years.As the monitoring environment becomes more complex and diversified,the data set collected by the sensor node is often accompanied by abnormal points.Most of the current prediction models do not filter the abnormal points.In order to effectively filter out abnormal points and improve the streamlining of data transmission and the accuracy of prediction,this paper proposes an exponentially weighted smoothing prediction model based on box-plot abnormal detection,and introduces a short-term chain ratio mechanism to determine emergencies.Experiments show that the model can effectively filter out abnormal points and determine emergencies under different data sets,smoothness coefficient changes and different dynamic thresholds.The streamlining of data transmission is increased by 5.8%,and the prediction accuracy is increased by 8.4%.Compared with the existing prediction models,it has better robustness and abnormal point processing ability.